A comparison of non-linear methods for the decoding of motor performance.

Simple item page
dc.contributor.advisorTangermann, M.W.
dc.contributor.advisorThielen, J.
dc.contributor.authorColdenhoff, J.M.
dc.date.issued2021-06-22
dc.description.abstractCurrently, work is underway to create a closed-loop deep brain stimulation (DBS) system. This research attempts to contribute to the creation of a closed-loop DBS system by improving motor labels used in the search for informative brain signals. This was done by building on earlier research that used a motor task called the copy-draw task, to evaluate ne-grained hand motor performance under DBS-on and DBS-o conditions. The earlier research used linear discriminant analysis (LDA) to reduce the large feature dimensionality to a single scalar value representing the motor score, which could then be used in conjunction with source power comodulation to nd informative brain signals. However, LDA makes the assumption that the underlying features distributions are Gaussian. We showed that this assumption is not met by the data, and we therefore tested whether logistic regression, random forest, and the support vector classi er, which do not make the assumption of Gaussian features yield better motor decoding performance. We evaluated the new methods using a nested cross-validation procedure with hyperparameter optimization. We also tested whether removing linear trends from the data, or splitting trials of the copy-draw task to create more training data yielded better motor decoding performance. We demonstrate that there is no signi cant increase in motor decoding performance by logistic regression, random forest, or support vector classi er. Moreover, we saw no increase in the motor decoding performance by using the data where linear trends were removed, or where the trials were split to create more training data. Our research therefore shows that in this problem setting LDA is robust to non-Gaussian features.
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15674
dc.language.isoen
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.thesis.typeBachelor
dc.titleA comparison of non-linear methods for the decoding of motor performance.
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Coldenhof, J. s-1017656-2021.pdf
Size:
7.28 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculteit der Sociale Wetenschappen